Abstract:
This study, titled "AI-Driven Bone Fracture Detection: Leveraging Image Processing and
Machine Learning on X-ray Images," embarks on enhancing the accuracy and efficiency
of diagnosing bone fractures using advanced AI techniques. Utilizing a dataset of X-ray
images augmented with metadata on patient demographics and clinical details, several deep
learning models, including VGG16, MobileNetV2, InceptionV3, ResNet50, and hybrid
combinations, were trained and validated. These models demonstrate substantial promise
in identifying and classifying bone fractures with varying degrees of precision. This study
gets a high accuracy of 89% in MobileNetV2 while using fully raw data. The research
highlights the superior performance of MobileNetV2 and hybrid models, which combine
the strengths of multiple neural network architectures to optimize fracture detection. By
integrating these AI models into clinical settings, the study aims to alleviate the workload
on radiologists, expedite diagnostic processes, and potentially enhance patient care by
offering rapid and accurate fracture evaluations. Moreover, the study explores the ethical
dimensions of AI deployment in medical diagnostics, focusing on data privacy, bias
mitigation, and system transparency. As the integration of AI in healthcare progresses, this
research paves the way for future explorations into expanding the models' capabilities to
other medical imaging modalities and developing real-time diagnostic tools. This work not
only advances the field of medical AI but also sets a benchmark for future research aimed
at refining AI-driven diagnostics in healthcare.
